Authors: Francis R. Bach, Michael I. Jordan.
Pusblished in Journal of Machine Learning Research 4 (2003), pp. 1205-1233.
Year: 2003.
Format: PDF.Abstract:
We present a generalization of independent component analysis (ICA), where instead of looking for a linear transform that makes the data components independent, we look for a transform that makes the data components well fit by a tree-structured graphical model. This tree-dependent component analysis (TCA) provides a tractable and flexible approach to weakening the assumption of independence of ICA. In particular, TCA allows the underlying graph to have multiple connected components, and thus the method is able to find "clusters" of components such that components are dependent within a cluster and independent between cluster. Finally, we make use of a notion of graphical models for time series due to Brillinger(1996) to extend these ideas to the temporal setting. In particular, we are able to fit models that incorporate tree-structured dependencies among multiple time series.
